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Real vs. Fake Image Classification for Production Pipeline
==========================================================
1\. Business Problem
--------------------
This project addresses the critical business need to automatically identify and flag manipulated or synthetically generated images. By accurately classifying images as **"real"** or **"fake,"** we can enhance the integrity of our platform, prevent the spread of misinformation, and protect our users from fraudulent content. This solution is designed for integration into our production pipeline to process images in real-time.
2\. Solution Overview
---------------------
This solution leverages OpenAI's CLIP (Contrastive Language-Image Pre-Training) model to differentiate between real and fake images. The system operates as follows:
1. **Feature Extraction:** A pre-trained CLIP model ('ViT-L/14') converts input images into 768-dimensional feature vectors.
2. **Classification:** A Support Vector Machine (SVM) model, trained on our internal dataset of real and fake images, classifies the feature vectors.
3. **Deployment:** The trained model is deployed as a service that can be integrated into our production image processing pipeline.
The model has achieved an accuracy of **98.29%** on our internal test set, demonstrating its effectiveness in distinguishing between real and fake images.
3\. Getting Started
-------------------
### 3.1. Dependencies
To ensure a reproducible environment, all dependencies are listed in the requirements.txt file. Install them using pip:
```bash
pip install -r requirements.txt
```
**requirements.txt**:
- numpy
- Pillow
- torch
- clip-by-openai
- scikit-learn
- tqdm
- seaborn
- matplotlib
### 3.2. Data Preparation
The model was trained on a dataset of real and fake images obtained form kaggle the dataset link is https://www.kaggle.com/datasets/tristanzhang32/ai-generated-images-vs-real-images/data$0.
### 3.3. Usage
#### 3.3.1. Feature Extraction
To extract features from a new dataset, run the following command:
```
python extract_features.py --data_dir /path/to/your/data --output_file features.npz
```
#### 3.3.2. Model Training
To retrain the SVM model on a new set of extracted features, run:
```
python train_model.py --features_file features.npz --model_output_path model.joblib
```
#### 3.3.3. Inference
To classify a single image using the trained model, use the provided inference script:
```
python classify.py --image_path /path/to/your/image.jpg --model_path model.joblib
```
4\. Production Deployment
-------------------------
The image classification model is deployed as a microservice. The service exposes an API endpoint that accepts an image and returns a classification result ("real" or "fake").
### 4.1. API Specification
* **Endpoint:** /classify
* **Method:** POST
* **Request Body:** multipart/form-data with a single field image.
* **Response:**
* JSON{ "classification": "real", "confidence": 0.95}
* JSON{ "error": "Error message"}
### 4.2. Scalability and Monitoring
The service is deployed in a containerized environment (e.g., Docker) and managed by an orchestrator (e.g., Kubernetes) to ensure scalability and high availability. Monitoring and logging are in place to track model performance, API latency, and error rates.
5\. Model Versioning
--------------------
We use a combination of Git for code versioning and a model registry for tracking trained model artifacts. Each model is versioned and associated with the commit hash of the code that produced it. The current production model is **v1.2.0**.
6\. Testing
-----------
The project includes a suite of tests to ensure correctness and reliability:
* **Unit tests:** To verify individual functions and components.
* **Integration tests:** To test the interaction between different parts of the system.
* **Model evaluation tests:** To continuously monitor model performance on a golden dataset.
To run the tests, execute:
```
pytest
```
7\. Future Work
---------------
* **Explore more advanced classifiers:** Investigate the use of neural network-based classifiers on top of CLIP features.
* **Fine-tune the CLIP model:** For even better performance, we can fine-tune the CLIP model on our specific domain of images.
* **Expand the training dataset:** Continuously augment the training data with new examples of real and fake images to improve the model's robustness.
8\. Contact/Support
-------------------
For any questions or issues regarding this project, please contact the Machine Learning team at [your-team-email@yourcompany.com](mailto:your-team-email@yourcompany.com) . |